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BE4M33SSU	Statistical Machine Learning
Roles:	PO, PV	Extent of teaching:	2P+2C
Department:	13133	Language of teaching:	EN
Guarantors:	Flach B.	Completion:	Z,ZK
Lecturers:	Drchal J., Flach B., Franc V.	Credits:	6
Tutors:	Drchal J., Flach B., Franc V.	Semester:	Z

Anotation:

The aim of statistical machine learning is to develop systems (models and algorithms) able to learn to solve tasks given a set of examples and some prior knowledge about the task. This includes typical tasks in speech and image recognition. The course has the following two main objectives

1.		to present fundamental learning concepts such as risk minimisation, maximum likelihood estimation and Bayesian learning including their theoretical aspects,
2.		to consider important state-of-the-art models for classification and regression and to show how they can be learned by those concepts.

Study targets:

The aim of statistical machine learning is to develop systems (models and algorithms) able to learn to solve tasks given a set of examples and some prior knowledge about the task.

Course outlines:

The course will cover the following topics - Empirical risk minimization, consistency, bounds - Kernel SVMs, RKHS, regression - Semi-supervised learning - Unsupervised learning, EM algorithm, mixture models - Bayesian learning - Deep (convolutional) networks and Boltzmann machines (graphical models) - Supervised learning for deep networks - Hopfield nets and energy minimisation (MAP in MRFs) - Structured output SVMs - Sampling methods, sampling from models - Ensemble learning, random forests

Exercises outline:

Labs will be dedicated to practical implementations of selected methods discussed in the course as well as seminar classes with task-oriented assignments.

Literature:

1.		M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, MIT Press, 2012
2.		K.P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012
3.		T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Springer, 2010

Requirements:

Prerequisites of the course are: - foundations of probability theory and statistics comparable to the scope of the course "Probability, statistics and information theory" (A0B01PSI), - knowledge of statistical decision theory foundations, canonical and advanced classifiers as well as basics of machine learning comparable to the scope of the course "Pattern Recognition and Machine Learning" (AE4B33RPZ)

Webpage:

http://cw.fel.cvut.cz/wiki/courses/be4m33ssu/start

Keywords:

machine learing, statistical learning

Subject is included into these academic programs:

Program	Branch	Role	Recommended semester
MEOI7_2018	Artificial Intelligence	PO	1
MEOI9_2016	Data Science	PO	3
MEOI5_2018	Computer Vision and Image Processing	PO	1
MEBIO3_2018	Image processing	PV	1
MPBIO3_2018	Image processing	PV	1
MEOI5_2016	Computer Vision and Image Processing	PO	1
MPBIO2_2018	Medical Instrumentation	PV	1
MEBIO_2018	Common courses	PV	1
MPOI8_2018	Bioinformatics	PO	3
MPBIO4_2018	Signal processing	PV	1
MPBIO1_2018	Bioinformatics	PV	1
MEOI7_2016	Artificial Intelligence	PO	1
MPOI9_2016	Data Science	PO	3
MPOI7_2016	Artificial Intelligence	PO	1
MEOI8_2016	Bioinformatics	PO	3
MPOI5_2018	Computer Vision and Image Processing	PO	1
MPOI9_2018	Data Science	PO	3
MEBIO_2018	Common courses	PV	1
MPOI8_2016	Bioinformatics	PO	3
MPOI5_2016	Computer Vision and Image Processing	PO	1
MEOI9_2018	Data Science	PO	3
MEOI8_2018	Bioinformatics	PO	3
MEBIO4_2018	Signal processing	PV	1
MPOI7_2018	Artificial Intelligence	PO	1

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Page updated 11.8.2020 17:51:51, semester: Z,L/2020-1, L/2019-20, Send comments about the content to the Administrators of the Academic Programs

Proposal and Realization: I. Halaška (K336), J. Novák (K336)